Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows125
Duplicate rows (%)1.0%
Total size in memory1.5 MiB
Average record size in memory130.0 B

Variable types

NUM14
BOOL2
CAT2

Reproduction

Analysis started2020-08-29 16:28:23.857119
Analysis finished2020-08-29 16:29:07.779037
Duration43.92 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 125 (1.0%) duplicate rows Duplicates
ExitRates is highly correlated with BounceRatesHigh correlation
BounceRates is highly correlated with ExitRatesHigh correlation
Administrative has 5768 (46.8%) zeros Zeros
Administrative_Duration has 5903 (47.9%) zeros Zeros
Informational has 9699 (78.7%) zeros Zeros
Informational_Duration has 9925 (80.5%) zeros Zeros
ProductRelated_Duration has 755 (6.1%) zeros Zeros
BounceRates has 5518 (44.8%) zeros Zeros
PageValues has 9600 (77.9%) zeros Zeros
SpecialDay has 11079 (89.9%) zeros Zeros

Variables

Administrative
Real number (ℝ≥0)

ZEROS

Distinct count27
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3151662611516626
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Memory size96.3 KiB
2020-08-29T11:29:07.884046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.321784106
Coefficient of variation (CV)1.434792897
Kurtosis4.701146249
Mean2.315166261
Median Absolute Deviation (MAD)1
Skewness1.960357209
Sum28546
Variance11.03424965
2020-08-29T11:29:08.008054image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0576846.8%
 
1135411.0%
 
211149.0%
 
39157.4%
 
47656.2%
 
55754.7%
 
64323.5%
 
73382.7%
 
82872.3%
 
92251.8%
 
Other values (17)5574.5%
 
ValueCountFrequency (%) 
0576846.8%
 
1135411.0%
 
211149.0%
 
39157.4%
 
47656.2%
 
ValueCountFrequency (%) 
271< 0.1%
 
261< 0.1%
 
244< 0.1%
 
233< 0.1%
 
224< 0.1%
 

Administrative_Duration
Real number (ℝ≥0)

ZEROS

Distinct count3335
Unique (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.81861053933592
Minimum0.0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Memory size96.3 KiB
2020-08-29T11:29:08.153070image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.2663691
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.7791075
Coefficient of variation (CV)2.187356431
Kurtosis50.55673905
Mean80.81861054
Median Absolute Deviation (MAD)7.5
Skewness5.615719019
Sum996493.468
Variance31250.85284
2020-08-29T11:29:08.310076image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0590347.9%
 
4560.5%
 
5530.4%
 
7450.4%
 
11420.3%
 
6410.3%
 
14370.3%
 
9350.3%
 
15330.3%
 
10320.3%
 
Other values (3325)605349.1%
 
ValueCountFrequency (%) 
0590347.9%
 
1.3333333331< 0.1%
 
2150.1%
 
3260.2%
 
3.54< 0.1%
 
ValueCountFrequency (%) 
3398.751< 0.1%
 
2720.51< 0.1%
 
2657.3180561< 0.1%
 
2629.2539681< 0.1%
 
2407.423811< 0.1%
 

Informational
Real number (ℝ≥0)

ZEROS

Distinct count17
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5035685320356853
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Memory size96.3 KiB
2020-08-29T11:29:08.460087image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.270156426
Coefficient of variation (CV)2.522310957
Kurtosis26.93226626
Mean0.503568532
Median Absolute Deviation (MAD)0
Skewness4.03646376
Sum6209
Variance1.613297346
2020-08-29T11:29:08.603098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0969978.7%
 
110418.4%
 
27285.9%
 
33803.1%
 
42221.8%
 
5990.8%
 
6780.6%
 
7360.3%
 
9150.1%
 
8140.1%
 
Other values (7)180.1%
 
ValueCountFrequency (%) 
0969978.7%
 
110418.4%
 
27285.9%
 
33803.1%
 
42221.8%
 
ValueCountFrequency (%) 
241< 0.1%
 
161< 0.1%
 
142< 0.1%
 
131< 0.1%
 
125< 0.1%
 

Informational_Duration
Real number (ℝ≥0)

ZEROS

Distinct count1258
Unique (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.47239792772304
Minimum0.0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Memory size96.3 KiB
2020-08-29T11:29:08.733108image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.7492944
Coefficient of variation (CV)4.082956304
Kurtosis76.31685309
Mean34.47239793
Median Absolute Deviation (MAD)0
Skewness7.579184716
Sum425044.6664
Variance19810.36388
2020-08-29T11:29:08.858118image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0992580.5%
 
9330.3%
 
6260.2%
 
10260.2%
 
7260.2%
 
13230.2%
 
12230.2%
 
8220.2%
 
16220.2%
 
11210.2%
 
Other values (1248)218317.7%
 
ValueCountFrequency (%) 
0992580.5%
 
13< 0.1%
 
1.51< 0.1%
 
2110.1%
 
2.51< 0.1%
 
ValueCountFrequency (%) 
2549.3751< 0.1%
 
2256.9166671< 0.1%
 
2252.0333331< 0.1%
 
2195.31< 0.1%
 
2166.51< 0.1%
 

ProductRelated
Real number (ℝ≥0)

Distinct count311
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.731467964314678
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Memory size96.3 KiB
2020-08-29T11:29:08.980125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.4755033
Coefficient of variation (CV)1.401621361
Kurtosis31.21170665
Mean31.73146796
Median Absolute Deviation (MAD)13
Skewness4.341516416
Sum391249
Variance1978.070394
2020-08-29T11:29:09.114142image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
16225.0%
 
24653.8%
 
34583.7%
 
44043.3%
 
63963.2%
 
73913.2%
 
53823.1%
 
83703.0%
 
103302.7%
 
93172.6%
 
Other values (301)819566.5%
 
ValueCountFrequency (%) 
0380.3%
 
16225.0%
 
24653.8%
 
34583.7%
 
44043.3%
 
ValueCountFrequency (%) 
7051< 0.1%
 
6861< 0.1%
 
5841< 0.1%
 
5341< 0.1%
 
5181< 0.1%
 

ProductRelated_Duration
Real number (ℝ≥0)

ZEROS

Distinct count9551
Unique (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.7462199688268
Minimum0.0
Maximum63973.522229999995
Zeros755
Zeros (%)6.1%
Memory size96.3 KiB
2020-08-29T11:29:09.302150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369047
Q31464.157213
95-th percentile4300.289077
Maximum63973.52223
Range63973.52223
Interquartile range (IQR)1280.019713

Descriptive statistics

Standard deviation1913.669288
Coefficient of variation (CV)1.601737052
Kurtosis137.1741637
Mean1194.74622
Median Absolute Deviation (MAD)500.9369047
Skewness7.263227683
Sum14731220.89
Variance3662130.143
2020-08-29T11:29:09.446162image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
07556.1%
 
17210.2%
 
8170.1%
 
11170.1%
 
15160.1%
 
19150.1%
 
22150.1%
 
12150.1%
 
7140.1%
 
13140.1%
 
Other values (9541)1143192.7%
 
ValueCountFrequency (%) 
07556.1%
 
0.51< 0.1%
 
12< 0.1%
 
2.3333333331< 0.1%
 
2.6666666671< 0.1%
 
ValueCountFrequency (%) 
63973.522231< 0.1%
 
43171.233381< 0.1%
 
29970.465971< 0.1%
 
27009.859431< 0.1%
 
24844.15621< 0.1%
 

BounceRates
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count1872
Unique (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138047072182
Minimum0.0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Memory size96.3 KiB
2020-08-29T11:29:09.608172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.0168125585
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.0168125585

Descriptive statistics

Standard deviation0.04848832181
Coefficient of variation (CV)2.185007006
Kurtosis7.723159431
Mean0.02219138047
Median Absolute Deviation (MAD)0.0031124675
Skewness2.947855267
Sum273.6197212
Variance0.002351117352
2020-08-29T11:29:09.760184image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0551844.8%
 
0.27005.7%
 
0.0666666671341.1%
 
0.0285714291150.9%
 
0.051130.9%
 
0.0333333331010.8%
 
0.0251000.8%
 
0.016666667990.8%
 
0.1980.8%
 
0.04960.8%
 
Other values (1862)525642.6%
 
ValueCountFrequency (%) 
0551844.8%
 
2.73e-051< 0.1%
 
3.35e-051< 0.1%
 
3.83e-051< 0.1%
 
3.94e-051< 0.1%
 
ValueCountFrequency (%) 
0.27005.7%
 
0.1833333331< 0.1%
 
0.185< 0.1%
 
0.1769230771< 0.1%
 
0.1751< 0.1%
 

ExitRates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count4777
Unique (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04307279776650446
Minimum0.0
Maximum0.2
Zeros76
Zeros (%)0.6%
Memory size96.3 KiB
2020-08-29T11:29:09.935196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.0251564025
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.04859654055
Coefficient of variation (CV)1.128242024
Kurtosis4.017034553
Mean0.04307279777
Median Absolute Deviation (MAD)0.0141725795
Skewness2.148789
Sum531.0875965
Variance0.002361623754
2020-08-29T11:29:10.078210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.27105.8%
 
0.13382.7%
 
0.053292.7%
 
0.0333333332912.4%
 
0.0666666672672.2%
 
0.0252241.8%
 
0.042141.7%
 
0.0166666671811.5%
 
0.021671.4%
 
0.0222222221521.2%
 
Other values (4767)945776.7%
 
ValueCountFrequency (%) 
0760.6%
 
0.0001755931< 0.1%
 
0.0002504381< 0.1%
 
0.0002621231< 0.1%
 
0.0002631581< 0.1%
 
ValueCountFrequency (%) 
0.27105.8%
 
0.1923076921< 0.1%
 
0.1888888892< 0.1%
 
0.1866666674< 0.1%
 
0.1833333332< 0.1%
 

PageValues
Real number (ℝ≥0)

ZEROS

Distinct count2704
Unique (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.889257862693592
Minimum0.0
Maximum361.76374189999996
Zeros9600
Zeros (%)77.9%
Memory size96.3 KiB
2020-08-29T11:29:10.258220image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.16052828
Maximum361.7637419
Range361.7637419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.56843661
Coefficient of variation (CV)3.152933195
Kurtosis65.63569361
Mean5.889257863
Median Absolute Deviation (MAD)0
Skewness6.382964249
Sum72614.54945
Variance344.7868381
2020-08-29T11:29:10.427233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0960077.9%
 
53.9886< 0.1%
 
42.293067523< 0.1%
 
40.278152442< 0.1%
 
12.558857142< 0.1%
 
44.893459372< 0.1%
 
58.92417662< 0.1%
 
16.15855822< 0.1%
 
10.999018442< 0.1%
 
21.21126552< 0.1%
 
Other values (2694)270722.0%
 
ValueCountFrequency (%) 
0960077.9%
 
0.0380345421< 0.1%
 
0.0670495461< 0.1%
 
0.0935469491< 0.1%
 
0.0986214031< 0.1%
 
ValueCountFrequency (%) 
361.76374191< 0.1%
 
360.95338391< 0.1%
 
287.95379281< 0.1%
 
270.78469311< 0.1%
 
261.49128571< 0.1%
 

SpecialDay
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061427412814274135
Minimum0.0
Maximum1.0
Zeros11079
Zeros (%)89.9%
Memory size96.3 KiB
2020-08-29T11:29:10.586246image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1989172732
Coefficient of variation (CV)3.238249245
Kurtosis9.91365887
Mean0.06142741281
Median Absolute Deviation (MAD)0
Skewness3.302666747
Sum757.4
Variance0.03956808156
2020-08-29T11:29:10.728255image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01107989.9%
 
0.63512.8%
 
0.83252.6%
 
0.42432.0%
 
0.21781.4%
 
11541.2%
 
ValueCountFrequency (%) 
01107989.9%
 
0.21781.4%
 
0.42432.0%
 
0.63512.8%
 
0.83252.6%
 
ValueCountFrequency (%) 
11541.2%
 
0.83252.6%
 
0.63512.8%
 
0.42432.0%
 
0.21781.4%
 

Month
Categorical

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.3 KiB
May
3364
Nov
2998
Mar
1907
Dec
1727
Oct
 
549
Other values (5)
1785
ValueCountFrequency (%) 
May336427.3%
 
Nov299824.3%
 
Mar190715.5%
 
Dec172714.0%
 
Oct5494.5%
 
Sep4483.6%
 
Aug4333.5%
 
Jul4323.5%
 
June2882.3%
 
Feb1841.5%
 
2020-08-29T11:29:10.965275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.023357664
Min length3

OperatingSystems
Real number (ℝ≥0)

Distinct count8
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.124006488240065
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size96.3 KiB
2020-08-29T11:29:11.113284image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9113248287
Coefficient of variation (CV)0.4290593432
Kurtosis10.45684261
Mean2.124006488
Median Absolute Deviation (MAD)0
Skewness2.066285042
Sum26189
Variance0.8305129434
2020-08-29T11:29:11.260294image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2660153.5%
 
1258521.0%
 
3255520.7%
 
44783.9%
 
8790.6%
 
6190.2%
 
770.1%
 
56< 0.1%
 
ValueCountFrequency (%) 
1258521.0%
 
2660153.5%
 
3255520.7%
 
44783.9%
 
56< 0.1%
 
ValueCountFrequency (%) 
8790.6%
 
770.1%
 
6190.2%
 
56< 0.1%
 
44783.9%
 

Browser
Real number (ℝ≥0)

Distinct count13
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.357096512570965
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size96.3 KiB
2020-08-29T11:29:11.398305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.717276676
Coefficient of variation (CV)0.7285559443
Kurtosis12.74673269
Mean2.357096513
Median Absolute Deviation (MAD)0
Skewness3.242349611
Sum29063
Variance2.94903918
2020-08-29T11:29:11.534316image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2796164.6%
 
1246220.0%
 
47366.0%
 
54673.8%
 
61741.4%
 
101631.3%
 
81351.1%
 
31050.9%
 
13610.5%
 
7490.4%
 
Other values (3)170.1%
 
ValueCountFrequency (%) 
1246220.0%
 
2796164.6%
 
31050.9%
 
47366.0%
 
54673.8%
 
ValueCountFrequency (%) 
13610.5%
 
12100.1%
 
116< 0.1%
 
101631.3%
 
91< 0.1%
 

Region
Real number (ℝ≥0)

Distinct count9
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1473641524736413
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size96.3 KiB
2020-08-29T11:29:11.690328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.401591237
Coefficient of variation (CV)0.7630484178
Kurtosis-0.1486803001
Mean3.147364152
Median Absolute Deviation (MAD)2
Skewness0.9835491595
Sum38807
Variance5.767640468
2020-08-29T11:29:11.849340image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1478038.8%
 
3240319.5%
 
411829.6%
 
211369.2%
 
68056.5%
 
77616.2%
 
95114.1%
 
84343.5%
 
53182.6%
 
ValueCountFrequency (%) 
1478038.8%
 
211369.2%
 
3240319.5%
 
411829.6%
 
53182.6%
 
ValueCountFrequency (%) 
95114.1%
 
84343.5%
 
77616.2%
 
68056.5%
 
53182.6%
 

TrafficType
Real number (ℝ≥0)

Distinct count20
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.069586374695864
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size96.3 KiB
2020-08-29T11:29:11.988348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.02516916
Coefficient of variation (CV)0.9890855703
Kurtosis3.479710597
Mean4.069586375
Median Absolute Deviation (MAD)1
Skewness1.962986732
Sum50178
Variance16.20198677
2020-08-29T11:29:12.118358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2391331.7%
 
1245119.9%
 
3205216.6%
 
410698.7%
 
137386.0%
 
104503.6%
 
64443.6%
 
83432.8%
 
52602.1%
 
112472.0%
 
Other values (10)3632.9%
 
ValueCountFrequency (%) 
1245119.9%
 
2391331.7%
 
3205216.6%
 
410698.7%
 
52602.1%
 
ValueCountFrequency (%) 
201981.6%
 
19170.1%
 
18100.1%
 
171< 0.1%
 
163< 0.1%
 

VisitorType
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.3 KiB
Returning_Visitor
10551
New_Visitor
 
1694
Other
 
85
ValueCountFrequency (%) 
Returning_Visitor1055185.6%
 
New_Visitor169413.7%
 
Other850.7%
 
2020-08-29T11:29:12.320376image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.09294404
Min length5

Weekend
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
False
9462
True
2868
ValueCountFrequency (%) 
False946276.7%
 
True286823.3%
 

Revenue
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
False
10422
True
 
1908
ValueCountFrequency (%) 
False1042284.5%
 
True190815.5%
 

Interactions

2020-08-29T11:28:29.195139image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:29.365150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:29.535165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:29.700179image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:29.864190image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:30.035202image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:30.196212image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:30.365226image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:30.572241image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:30.845260image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.006274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.177285image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.347296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.531311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.724347image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:31.905339image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:32.085352image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:32.267365image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:32.475380image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:32.664397image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:32.857408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:33.065424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:33.272441image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:33.469453image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:33.654469image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:33.846483image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.014494image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.195507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.405524image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.580537image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.748551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:34.950565image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:35.123576image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:35.283592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:35.469604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:35.661618image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:35.851631image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:36.022645image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:36.188657image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:36.515680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:36.697694image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:36.897709image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.088726image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.249734image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.440748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.606761image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.769559image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:37.950573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:38.126551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:38.336562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:38.550563image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:38.718560image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:38.883573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.048568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.207566image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.373559image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.566952image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.743966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:39.934979image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:40.103992image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:40.303009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:40.496021image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:40.692035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:40.893051image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.091064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.279078image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.460093image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.632107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.804119image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:41.982130image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:42.201148image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:42.423164image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:42.605178image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:42.784190image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:42.962204image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:43.172219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:43.582251image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:43.788265image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:43.985280image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:44.180292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:44.400311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:44.599325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:44.773337image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:44.951349image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:45.132364image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:45.334380image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:45.550394image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:45.735409image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:45.957427image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:46.146440image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:46.367458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:46.578471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:46.759485image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:46.940499image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:47.106509image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:47.293526image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:47.539543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:47.778561image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:47.975574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:48.181591image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:48.389605image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:48.587622image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:48.777639image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:48.974648image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:49.163662image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:49.355678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:49.585695image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:49.779707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:49.979721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:50.210742image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:50.406754image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:50.634773image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:50.868790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.034800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.191812image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.382828image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.538839image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.714850image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:51.912866image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:52.100880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:52.514912image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:52.703925image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:52.888941image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.081953image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.252966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.419976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.586988image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.746001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:53.926013image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.079026image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.217037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.436052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.595065image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.763076image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:54.944090image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:55.133105image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:55.322117image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:55.514136image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:55.701145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:55.903163image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:56.080172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:56.242185image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:56.471201image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:56.657218image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:56.830229image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:57.021241image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:57.194257image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:57.432273image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:57.631288image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:57.842303image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.006315image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.168327image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.344343image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.530357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.711372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:58.878383image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.058395image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.213406image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.372419image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.547431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.729444image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:28:59.930460image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.101469image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.252482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.443498image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.613509image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.784523image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:00.964535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:01.138546image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:01.340564image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:01.547581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:01.736595image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:01.934605image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:02.128621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:02.322635image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:02.528649image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:02.999684image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:03.171698image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:03.350710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:03.532725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:03.709737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:03.941753image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:04.127769image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:04.333784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:04.527803image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:04.734812image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:04.915825image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:05.119840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:05.314856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:05.536873image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:05.740889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:05.908903image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:06.075911image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:06.260926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:06.444941image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:06.641955image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-29T11:29:12.475387image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-29T11:29:12.831415image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-29T11:29:13.203442image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-29T11:29:13.590466image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-29T11:29:14.286518image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-29T11:29:07.030982image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-29T11:29:07.540020image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.0Feb1111Returning_VisitorFalseFalse
100.000.0264.0000000.0000000.1000000.00.0Feb2212Returning_VisitorFalseFalse
200.000.010.0000000.2000000.2000000.00.0Feb4193Returning_VisitorFalseFalse
300.000.022.6666670.0500000.1400000.00.0Feb3224Returning_VisitorFalseFalse
400.000.010627.5000000.0200000.0500000.00.0Feb3314Returning_VisitorTrueFalse
500.000.019154.2166670.0157890.0245610.00.0Feb2213Returning_VisitorFalseFalse
600.000.010.0000000.2000000.2000000.00.4Feb2433Returning_VisitorFalseFalse
710.000.000.0000000.2000000.2000000.00.0Feb1215Returning_VisitorTrueFalse
800.000.0237.0000000.0000000.1000000.00.8Feb2223Returning_VisitorFalseFalse
900.000.03738.0000000.0000000.0222220.00.4Feb2412Returning_VisitorFalseFalse

Last rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0Nov2231Returning_VisitorFalseFalse
1232100.0000.060.0000000.2000000.2000000.0000000.0Nov1841Returning_VisitorFalseFalse
12322676.2500.0221075.2500000.0000000.0041670.0000000.0Dec2242Returning_VisitorFalseFalse
12323264.7500.0441157.9761900.0000000.0139530.0000000.0Nov22110Returning_VisitorFalseFalse
1232400.0010.016503.0000000.0000000.0376470.0000000.0Nov2211Returning_VisitorFalseFalse
123253145.0000.0531783.7916670.0071430.02903112.2417170.0Dec4611Returning_VisitorTrueFalse
1232600.0000.05465.7500000.0000000.0213330.0000000.0Nov3218Returning_VisitorTrueFalse
1232700.0000.06184.2500000.0833330.0866670.0000000.0Nov32113Returning_VisitorTrueFalse
12328475.0000.015346.0000000.0000000.0210530.0000000.0Nov22311Returning_VisitorFalseFalse
1232900.0000.0321.2500000.0000000.0666670.0000000.0Nov3212New_VisitorTrueFalse

Duplicate rows

Most frequent

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenuecount
2600.000.010.00.20.20.00.0Mar2211Returning_VisitorFalseFalse14
3600.000.010.00.20.20.00.0Mar3231Returning_VisitorFalseFalse7
4400.000.010.00.20.20.00.0May2213Returning_VisitorFalseFalse7
3800.000.010.00.20.20.00.0May1113Returning_VisitorFalseFalse6
1300.000.010.00.20.20.00.0Dec813920OtherFalseFalse5
3400.000.010.00.20.20.00.0Mar3211Returning_VisitorFalseFalse4
4100.000.010.00.20.20.00.0May1143Returning_VisitorFalseFalse4
6000.000.010.00.20.20.00.0Nov2211Returning_VisitorFalseFalse4
000.000.010.00.20.20.00.0Dec1111Returning_VisitorTrueFalse3
300.000.010.00.20.20.00.0Dec1141Returning_VisitorTrueFalse3